Course description:The purpose of this intensive workshop is threefold: to create a foundational experience in E&I for all, to empower participants to identify and solve real-world problems with technology, and to instill an entrepreneurial “can-do” attitude in the culture of this first cohort. Participants engage in experiential learning to prototype whole technology innovations. That is, they iterate all the components of an innovation: the problem to solve, the technology to solve it, the possibilities for impact, and the vehicle to bring the proposed innovation to life.
Prerequisites:N/A

6

Ilia Dubinsky

Professor

2

MA06066

Earth Observation Sensors and Measurements

Course description:This course introduces students to the first principles and methods of the observation of Earth surface, monitoring of Earth atmosphere and detection of different kind of radiation coming from Space. The course will cover wide range of the satellites-, aircraft-, rockets- and balloon- based techniques designed for environmental monitoring, meteorology, map making etc. The course will teach the theoretical fundamentals required for the design of instruments for Earth observation. The course will cover the wide range of sensors used for observation including optical, acoustic, electro-chemical, mass spectrometry based, magnetic, etc.After successful completion of this class, students will acquire the initial knowledge of the composition and structure of the atmosphere (gases, radicals, ions, aerosols at different altitudes), solar wind, cosmic rays, temperature distribution and magnetic field distribution.Students will acquire knowledge about different sensors design and range of their applications and the body of knowledge required to design of instruments for Earth observation at the level of Preliminary Design Review (PDR).
Prerequisites:Basic undergraduate mathematics including, linear algebra, real variables analysis, complex variables, linear algebra, differential equations, basic undergraduate physics electro-magnetism, mechanics.

6

Evgeny Nikolaev

Professor

3

MA03135

Graphical Models of Statistical Inference

Course description:This course is recommended for IT students, as well as other specialization students (e.g. Energy & Bio), interested in learning about modern theoretical and practical approaches to analysis of big data sets with reach statistical correlations expressed through graphs, matrices, tensors and related. The course is light on rigorous proofs, but rich on statistics and physics intuition.
Prerequisites:This is an advanced level course suitable for second year M.Sc. and Ph.D. students.Knowledge of basic math (algebra, analysis, differential equations) is assumed/required. Some prior experience in Probability Theory, Statistics, Statistical Mechanics or Machine Learning (at least one credited course) is recommended.

3

Michael Chertkov

Professor

4

PA06107

Green's Function Methods in Condensed Matter Physics

Course description:The course covers applications of Green’s functions to condensed matter physics. The goal is to illustrate how the formalism works by considering instructive examples. Lectures will start with the applications of Green’s functions to problems of basic quantum mechanics and then proceed to more sophisticated problems of condensed matter physics..
Prerequisites:Students specializing in condensed matter theory and experiment

6

Anton Andreev

Invited (UW, USA)

5

PA06108

High-Temperature Superconductors

Course description:TBD
Prerequisites:TBD

6

Boris Fine

Associate Professor

6

MA03111

Introduction to Data Science

Course description:The course gives an introduction to the main topics of modern data analysis such as classification, regression, clustering and dimensionality reduction. Each topic is accompanied with overview of the machine learning algorithms solving the problem and is illustrated with a set of real-world examples. Special attention is paid to the modern data analysis libraries which allow to efficiently solve the aforementioned problems.
Prerequisites:N/A

3

Mikhail Belyayev; Maxim Panov

Research Scientist

7

MA06064

Introduction to Petroleum Engineering

Course description:The course is an introduction to Petroleum Engineering and gives an overview of Petroleum Engineering and its various components and their internal connection.The course will address the story of oil from its origin to the end user. The objective is to provide an overview of the fundamental operations in exploration, drilling, production, processing, transportation, and refining of oil and gas.
Prerequisites:N/A

6

Bahman Tohidi

Invited (Heriot Watt, UK)

8

MA06099

Materials Selection in Design

Course description:This is a newly developed course which illustrates the need for a scientific and effective method of materials selection and how it fits into engineering design. Students learn and review the principles of materials science including materials classification, microstructure, properties, and performance of mechanical engineering design materials (metals, ceramics, plastics) and processing effects. They will also learn the new material selection scheme developed by Professor Ashby for optimal selection of materials for specific applications.
Prerequisites:Basic knowledge of Materials Science, and Mechanics of Materials.

Course description:This is an advanced course of molecular biology. Structure of the course is based on the flow of information in gene expression. The first part of the course is devoted to DNA biosynthesis, including replication, recombination and repair. Additionally, introduction into representative phage and viral replication will be provided. Second part of the course goes about RNA biosynthesis and includes transcription and processing. The third part of the course is about protein biosynthesis.
Prerequisites:The course requires a biological background, such as BS level knowledge of biochemistry, molecular and cell biology.

3

Petr Sergiev

Associate Professor

11

PA06109

Nano-Optics

Course description:Nano-optics aims at the understanding of optical phenomena on the nanometer scale, i.e. near or beyond the diffraction limit of light. Typically, elements of nano-optics are scattered across the disciplines. Nano-optics builds on achievements of optics, quantum optics, and spectroscopy. In the presence of an inhomogeneity in space the Rayleigh limit for the confinement of light is no longer strictly valid. In principle infinite confinement of light becomes possible, at least theoretically.The course will cover basic theoretical concepts (angular spectrum representation, Green’s function methods, and diffraction limit), multiphoton microscopy, interaction of light with nanoscale systems (artificial quantum structures, molecules, and proteins), optical interaction between nanosystems, and resonance phenomena (localized surface plasmons, surface plasmon polaritons, microresonators).
Prerequisites:Undergraduate level Electricity and Magnetism; Undergraduate level Quantum Mechanics

6

Vladimir Drachev

Professor

12

MA06113

Scientific Computing

Course description:Topics:

High-Performance Computing (HPC)

Introduction: history of HPC, terminology of HPC, main areas of applications, current trends

Compromise between scalability and convergence rate

Memory greedy vs CPU/GPU greedy methods

A list of most important equations we solve with use of HPC, corresponding models and most popular numerical methods

Three main sources of errors: numerical (solvers), error of the model (due approximation of “reality”), statistical (due to imperfect sampling)

Course description:The course teaches fundamentals of modern Materials Science (Part I of the course) and provides a survey of materials (Part II), covering all relevant Skoltech research areas and beyond, with brief explanation of structural, electronic, physical, chemical or other properties of materials relevant for their practical use, or from the point of view of utilizing their unique properties in applications.Prerequisites:Basic knowledge of theoretical physics or chemistry including quantum mechanics and statistical physics at undergraduate level.

6

Andriy Zhugayevych

Assistant Professor

16

MA06138

Theory of Phase Transitions

Course description:TBD
Prerequisites:TBD

6

Vladimir Lebedev

Invited (MIPT, Russia)

Term 2 (October 24 – December 16, 2016)

17

MA06068

Advanced Solid State Physics

Course description:The course is a part of the educational program in quantum materials. It can also be chosen as an elective for the programs in photonics and material science.Prerequisites:Basic knowledge of quantum mechanics and statistical physics;Recommended courses: Course «Introduction to Solid-State Physics», or a comparable course.

6

Boris Fine

Associate Professor

18

MA03022

Basic Molecular Biology Techniques

Course description:The course provides hand-on experience with the general techniques used in the molecular biology/biotechnology lab. The goal of the course is to teach students good laboratory practice and rational planning of the experimental work.There are three different levels to the course (basic, advanced and research).Prerequisites:The course requires basic knowledge of molecular and cellular biology.

3

Konstantin Severinov

Professor

19

MA03065

Bioinformatics Lab Course

Course description:The course will introduce students to basic bioinformatics tools necessary for all molecular biologists. The course covers a broad range of issues, in particular: main databases, sequence alignment, genome annotation, analysis of protein structure and function, analysis of RNA structure, construction of phylogenetic trees.The course includes lectures, practical analysis conducted by students in front of computer, homework. At the end, students will perform independent analysis of newly sequenced genome fragments using the array of tools learned during the course.Prerequisites:Knowledge of basic molecular biology. This is a core course.

3

Mikhail Gelfand

Professor

20

MA06008

Computational Chemistry and Materials Modelling

Course description:The course provides an overview of modern atomistic computer simulations used to model, understand and predict properties of realistic materials. The emphasis is on practical techniques, algorithms and programs to bridge theory and applications, from the discovery of materials to their use in real-world technologies. This introductory course is intended for both theoreticians and experimentalists in modern Materials Science.Prerequisites:Recommended courses: Materials Chemistry or similar courseRecommended knowledge: quantum mechanics and statistical physics

6

Andriy Zhugayevych

Assistant Professor

21

MA03136

Convex Optimization for Data Science

Course description:This course gives basic knowledge of contemporary numerical methods for solving large-scale and huge-scale convex optimization problems. Most of these problems go back to the Data Science. In particular, we plane to demonstrate the power of different variants of stochastic gradient descents for such problems. Course required high mathematical skills in probability theory and linear algebra.Required topics:

Course description:This course will provide a graduate level overview of modern energy systems, covering generation, conversion, transportation and end-use energy technologies. For each set of technologies we will first review the fundamental physics principles that are already extensively covered in most of the Russian undergraduate technical department curriculums. Next, we will assess the engineering challenges associated with the technologies, and discuss how to do basic cost-benefit analysis of possible approaches. Each section will be concluded by the analysis of modern trends in the areas and discussion of possible innovation and research opportunities. The pedagogy will include overview lectures and homework covering the more fundamental part of the material, as well as individual projects focusing on the analysis of novel technologies proposed in academic papers or introduced by various innovative companies. Guest lectures of industry representatives will present the major industrial company’s perspective on the key challenges and opportunities.Prerequisites:Undergraduate physics

6

Aldo Bischi

Assistant Professor

24

PE06101

History and Philosophy of Science

Course description:The course “History and Philosophy of Science” comprises ten topics covering the most important events in the history of science and the reflections of the leading philosophers on the ways of scientific progress (“cumulative and “revolutionary” hypotheses). The main attention is paid to the crucial points in the development of science: 1) the birth of science in Ancient Greece; 2) translation of ancient science in the medieval period; 3) renaissance science; 4) Galileo case; 5) scientific revolutions of the XXth century. The course also includes some reflections on the nuclear energy and the impact of Internet on societyCourse goal: give PhD students a general view about the birth, development and perspectives of science; prepare them for the qualifying examinations for the Candidate degree.Course structure: lectures, non-laboratory based activities, oral examinationPrerequisites:Masters or Specialist degree in engineering, technology, natural sciences;

6

Ivan Lupandin

Invited (MIPT, Russia)

25

MC06002

Ideas to Impact

Course description:Technological Innovation is critical to the survival and competitiveness of emerging and existing organizations. This course lays the foundation to undertake a robust analysis and design of opportunities for technology-based commercialization. We introduce tools and frameworks that help isolate and control the factors shaping the identification, evaluation and development of commercial opportunities. Throughout the course we use technology examples originating from problem sets found in engineering and scientific education to develop the skills necessary to connect technology and impact.The course is designed to help students develop the ability to find, evaluate, and develop technological ideas into commercially viable product and process concepts, and build those concepts into viable business propositions. The material covered is research and theory-based but the course is practice-oriented with much of the term spent on shaping technology-based opportunities. A central objective of this subject is to equip students with an understanding of the main issues involved in the commercialization of technological advances at both strategic and operational levels.Prerequisites:N/A

6

Zelijko Tekic

Assistant Professor

26

MA03118

Imaging in Biology

Course description:Overview of current imaging research techniques in basic biomedical research. Various applications in neurobiology, cancer biology and preclinical studies of novel and emerging advanced microscopy technologies. Analysis of experiments and research described in recent scientific papers. Introduction of the course also includes core mathematics, optics and nuclear physics. Atop of studying different optical microscopy techniques and super-resolution imaging the course will outline and compare the roles of CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography) in biomedical research.Topics also include clearing agents and techniques, optical imaging of brain activity in vivo using genetically encoded probes, immediate early gene mapping, intravital imaging, applications for functional analyses of neuronal circuits.The course aims to teach students to understand basic principles of the current imaging techniques, microscope design and image formation. The course will also offer a practice in image analysis with open source software. Students will learn how to choose the most appropriate imaging method for their own research project.Prerequisites:BS level knowledge of general physics, molecular, cellular biology and neurobiology.

Introduction to the Biomedical Industry and Functional Genomics (Functional Genomics)

Course description:The course emphasizes functional genomics as essential element of drug discovery, target verification and mechanistic evaluation of drug action. It covers all aspects of the field: basic methods in integrative physiology and contemporary imaging methods on molecular, cellular, organ and whole animal levels. Cardiovascular, renal, pulmonary, immunological, neural and cancer models will be discussed with illustration of major in vivo protocols.The goal of the course is to give the students with biological, chemical, physical, mathematical and engineering background the in-depth knowledge and understanding unraveling causative factors of disease and targeted intervention using genetic intervention and integrative physiology approach.Prerequisites:High through put analysis of transcriptome, principles of genetic modifications, various vectors for transgenes, specific recombination in ES cells (reporter knock ins, knockouts, conditionsl knockouts), genome editing (TALEN, CRISPR). Special attention is given to RNAi translational medicine.

3

Victor Koteliansky

Professor

29

MA06116

Material Structure Characterization Methods

Course description:The course teaches theoretical and practical fundamentals of diffraction and electron microscopy methods applied to the analysis of the crystal structure, nano- and microstructure of materials.The course delivers basic knowledge on the theory of crystal structure analysis with various kinds of radiation, modern techniques of crystal structure determination, the analysis of the local structure of matter, defects and microstructure, theory of image formation in the electron microscope and a review on modern spectroscopic techniques with atomic resolution. The competences acquired in this course can be further used in all branches of material science dealing with crystalline matter.The course consists of lectures, seminars/practical lessons, laboratory works and exam.Prerequisites:Survey of Materials course, basic knowledge on diffraction physics, geometrical optics and crystallography.

6

Artem Abakumov

Professor

30

MA03033

Mathematical Modeling in Biology

Course description:The course aims to teach students to quantify biological observations into conceptual models, frame these models in mathematical terms, and analyze these models, both qualitatively and numerically.It includes consideringStrategies to choose the relevant variables, parameters and observables, model nature (discrete vs continuous), modeling technique (agent-based simulations vs. dynamical system approach), and visualization and interpretation of the results. The following classes of systems will be used as examples:Population modelsEvolution and speciationReaction-diffusion systems and pattern formationNetworksGame-theoretical modelsPrerequisites:Calculus, differential equation, and probability, and programming skills in a language of choice with Matlab as the default.

Course description:A research paper-based course where recent papers in the field are presented by students and critically discussed by the class led by instructor.
Prerequisites:General Molecular Biology course.

2

Konstantin Severinov

Professor

34

MA06024

Numerical Linear Algebra

Course description:Numerical linear algebra forms the basis for all modern computational mathematics. It is not possible to develop new large scale algorithms and even use existing ones without knowing it.In this course I will show, how numerical linear algebra methods and algorithms are used to solve practical problems.Matrix decompositions play the key role in numerical linear algebra. We will study different matrix decompositions in details: what are they, how to compute them efficiently and robustly, and most importantly, where they are applied to the solution of linear systems, eigenvalue problems and several others. For large-scale problems iterative methods will be described.I will try to highlight recent developments when it is relevant to the current lecture.This course should serve as a basis for several other IT Skoltech courses, including Fast PDE, Optimization and Great Computational methods. It will also serve as a first-time where programming environment and infrastructure is introduced in a consistent manner.
Prerequisites:Basic Calculus, basic linear algebra knowledge. The assignments will involve computer programming. The main language will be Python, and the assignments will be distributed in the form of IPython notebooks. We will spend 1lecture or 2 (depending on the actual progress) on the technical details on the Python ecosystem (syntax, plotting, basic libraries, sharing code).

6

Ivan Oseledets

Associate Professor

35

MA06002

Optimization Methods

Course description:This course is an application-oriented introduction to optimization. It will focus on modeling real-world engineering tasks as optimization problems and using state-of-the-art optimization techniques to solve the resulting models. The course will provide appropriate theoretical background and will go through a range of standard optimization models (e.g. Linear programs), as well as advanced optimization techniques required to handle more challenging optimization tasks. Application domains will include (among others) information engineering/machine learning, power engineering, transportation and logistics, structural design.
Prerequisites:Linear algebra, MATLAB or Python coding skills.

6

Victor Lempitsky

Associate Professor

36

PE03100

Pedagogy of Higher Education

Course description:The aim of the course is to prepare PhD students for teaching at higher education institutions in accordance with the requirements of the «International Engineering Educator ING. PAED. IGIP» Standard and FSES with a focus on the Professional Standard “Teacher of Vocational Training, Higher Education and Continues Professional Development (Reg. Number 514)” approved by the RF Ministry of Labor and Social Protection on 08 September, 2015, № 608 н. Skoltech PhD students are prepared for the development and delivery of higher educational programs relevant to requirements of international and Russian standards, with the use of the results of PhD student’s research.
Prerequisites:PhD course

Course description:The overview of basic principles, goals and role of photonics in modern technology is presented. The course is designed to give the students a general understanding of the photonics role for modern society, mechanisms allowing to control light-matter interaction and main directions of the application of light-based technologies. The medicine, telecom, sensoring, manufacturing and several other applications of light will be addressed and the advantages achieved in these fields will be explained.
Prerequisites:N/A

6

Nikolay Gippius

Professor

39

MA06050

Robotics

Course description:The lecture course introduce you to basics knowledge of methods for robot design, simulation, and control of robotic system. Topics include robot kinematics, dynamics, control, design, simulation, motiond planning, and AI. The course slightly touches the research in wearable, space and telexistence robotics. The projects in robotics done by the lecturer, such as NurseSim, iFeel_IM!, FlexTorque, NAVIgoid, TeleTA will be presented. The lecture aims at student preparation and motivation to conduct projects in Robotics, Automation, Advanced manufacturing, and Intelligent Systems.
Prerequisites:Matrix algebra, programming skills.

6

Dzmitry Tsetserukou

Assistant Professor

40

MA06074

Spacecraft and Mission Design

Course description:This course introduces students to satellite engineering and provides theoretical fundamentals required for the design of a space mission. The course will teach design fundamentals of satellite subsystems, including payload, thermal, structures and configuration, communications, avionics, power, propulsion, ADCS, orbits, and ground systems. The theory is applied to a group design project of a space mission for novel terrestrial applications or space science purposes. After successful completion of this class, students will have acquired the body of knowledge required to design a space mission at the Preliminary Design Review (PDR) level.
Prerequisites:No previous space engineering knowledge is required. All Skoltech students are welcome to take this course. However, basic programming skills in a programming language of choice are required.

6

Alessandro Golkar

Associate Professor

41

MA06067

Structural Analysis and Design

Course description:This course is developed to give students a background into the general issues of the design and operation of structures and components.Design process/concepts and philosophies are discussed. Requirements, loads, design allowables, case study, environmental effects are considered referencing to advanced performance structures. CAD and CAE for design and analysis of structural elements are observed.Analytical topics include stress-strain behavior of the isotropic (mostly) and anisotropic (briefly) materials, and strength analysis. Specific topics include analysis methods for bolt and welding joints.Theoretical models are discussed for application to the analysis of experimental techniques used for characterizing of structural materials.Lectures are supplemented by laboratory sessions in which material characterization tests are performed.For calculation practice a study of commercial finite element software via modeling and solution of typical problems is included as one of principal parts of the course. A brief introduction in theory of finite element analysis is included. Bending, buckling, and vibration of beams and columns (typically I, O, T, L, and C cross sections) as typical structural elements and examples of bolt joints are modeled and analyzed in the framework of finite element analysis practice.
Prerequisites:Basics of solid mechanics, basics of calculus.

6

Ivan Sergeichev

Senior Research Scientist

Term 3 (January 30 – March 24, 2017)

42

MA03046

Advanced Molecular Biology Techniques

Course description:This project-based course provides experience with scientific project planning and implementation in the molecular biology/biotechnology lab. Course-based research projects are geared towards the background and experience of students.The goal of the course is to teach students good laboratory practice and rational planning of the experimental work.
Prerequisites:Basic Molecular Biology Techniques course. Basic knowledge of molecular and cellular biology as well as minimal wet lab experience.

Course description:The course provides hand-on experience with the general techniques used in the molecular biology/biotechnology lab. The goal of the course is to teach students good laboratory practice and rational planning of the experimental work.There are three different levels to the course (basic, advanced and research).
Prerequisites:The course requires basic knowledge of molecular and cellular biology.

3

Konstantin Severinov

Professor

45

MA06125

Biostatistics

Course description:This introductory course to statistics and probability theory covers a broad spectrum of topics, while keeping the spirit of quantitative discourse applied to real-life problems, and with a special focus on biomedical applications. The material is offered in 5 consecutive modules, each containing a lecture and a practicum class. For practical exercises, we use R programming language and R-Studio software. However, this course is focused on statistics rather than R; therefore, each practicum is designed with the purpose to demonstrate and reinforce understanding of basic statistical concepts introduced in the lecture rather than to provide a training in R.
Prerequisites:N/A

6

Dmitri Pervouchine

Assistant Professor

46

MA06005

CSE I: Modelling and Simulation

Course description:Many complex systems developed by engineers (e.g. labs on chips, iPads, magnetic resonance imaging scanners, nationwide electrical/gas/oil transportation networks, or buildings/automotive/aircraft frames or supply chains and economical/social networks) or found in nature (e.g. the human cardiovascular system, the brain neural network, biological systems, or the geophysical network of oil/water/gas reservoirs) can be viewed as large collections of interconnected dynamical system components. The performance and characteristics of each individual component critically depends on what engineers or scientists refer to as second order effects, and can be captured only by resorting to expensive partial differential equation solvers. In this course we will survey several techniques for modeling and simulation of a large variety of engineering and physical complex systems. In particular, detailed examples will be presented, drawn from the following engineering disciplines: Electrical Engineering (interconnect networks including parasitics; electromagnetic structures; analog and digital circuits including nonlinear semiconductor devices and Micro-Electro-Mechanical Devices), Mechanical Engineering (frame modeling, heat diffusion, fluid-dynamics and oil transport), Civil Engineering (structural problems, vibrations), Material Sciences (inverse problems for identification of material properties), Biomedical Engineering (human cardio-vascular system).This course provides students access to the state of the art in numerical tools in order to help them with their research projects involving analysis, design and optimization problems in a variety of different engineering and science disciplines dealing with complex systems. The focus of the course will not be on mathematical formalism and rigorous theorem proving, but rather on developing general intuition and practical implementation skills.
Prerequisites:Numerical Linear Algebra

6

Thanos Polimeridis

Assistant Professor

47

MA03049

Developmental Biology

Course description:The course focuses on molecular mechanisms of development and differentiation in invertebrate and vertebrate model systems, including embryonic stem cells. Attention will be given to major signaling pathways, gene regulatory networks and quantitative models.The goal of the course is to provide the students an overview of principles of development across model systems. This basic knowledge is essential for regenerative biology and biotechnology.
Prerequisites:The course requires certain knowledge of biology, including basics of embryology and molecular biology. Desirably, students need to have a minimal understanding of mathematics, including calculus and differential equations.

3

Dmitri Papatsenko

Assistant Professor

48

MA06083

Dynamic Systems and Control

Course description:The course focuses on dynamic systems, and their control. Such systems evolve with time and have inputs, disturbance, and outputs. One can find examples of dynamic systems in everyday life, for examples, automobiles, aircrafts, cranes, electrical circuits, fluid flow. You will analyze the response of these systems to input. Students will learn how to control system through feedback to ensure desirable dynamic properties (performance, stability). The practice will include work with industrial, humanoid, mobile, and telepresence robot.
Prerequisites:Basic computer science principles and skills, basic probability theory, basics of Matlab.

6

Dzmitry Tsetserukou

Assistant Professor

49

MA03124

Evolutionary and Medical Genomics

Course description:This course introduces the fundamentals of evolutionary science as applied to genomics. It will allow seeing how the basic population genetics processes create, maintain and affect variability in populations and lead to their changes with time. The focus will be on molecular evolution, i.e., the manifestation of these processes in genomes. As humans, we will be particularly interested in evolutionary aspects of medicine. The course assumes no prior familiarity with evolutionary biology, although knowledge of the basics of molecular biology and genetics is expected. The themes covered will include basic concepts in evolutionary biology and generalizations in evolutionary genomics; population genetics and factors of microevolution; and basics of quantitative genetics. The course will alternate lectures with reading and student-guided discussion of recent literature in the field. There will be more lecturing near the beginning of the course, and more discussing near its end.
Prerequisites:Introductory course on molecular biology.

Course description:The course emphasizes genetic in vivo models as essential element of drug target discovery, verification and mechanistic evaluation. It covers all aspects of the field: principles of genetic modifications, various vectors for transgenes, specific recombination in ES cells (reporter knock ins, knockouts, conditionsl knockouts) , genome editing (TALEN, CRISPR). Special at attention is given to RNAi translational medicine. It also gives basic methods in integrative physiology and contemporary imaging methods on molecular, cellular, organ and whole animal levels. Cardiovascular, renal, pulmonary, immunological, neural and cancer models will be discussed with illustration of major in vivo protocols.The goal of the course is to give the students with biological, chemical, physical, mathematical and engineering background the in-depth knowledge and understanding unraveling causative factors of disease and targeted intervention using genetic intervention and integrative physiology approach.
Prerequisites:Knowledge of molecular biology, cellular biology, medical physiology, histology.

3

Yuri Kotelevtsev

Professor

53

MA06122

Information and Coding Theory

Course description:The aim of the course is to explain basic ideas and results of information and coding theory, some of which has been used for rather long time in data science, in particular various entropy inequalities, and some emerged just very recently, for instance, usage of error-correcting codes for improvements of k-means method for clustering. The course is divided into two parts: introduction to information theory and elements of modern coding theory. In the first part, we consider the measure of information, mutual information, entropy, evaluation of channel capacity for single user and multi-user channels. In the second part, we consider foundations of coding theory such as block codes, linear codes, bounds on the code’s parameters and the most popular algebraic coding methods (Hamming, Reed-Muller, BCH and Reed-Solomon codes). Then we consider modern coding techniques, i.e. iterative decoding systems and graphical models to represent them. Iterative techniques have revolutionized the theory and practice of coding and have been applied in numerous communications standards. We discuss low-density parity-check (LDPC) codes, factor graphs and Sum-Product decoding algorithm.
Prerequisites:Linear algebraProbability theoryGraph theoryProgramming

6

Alexey Frolov

Senior Research Scientist

54

MC06006

Intellectual Property and Technological Innovation

Course description:Intellectual property (IP) is a critically important aspect of technological innovation and a key factor in the management of technology-intensive enterprises. Prowess in the management of intellectual property is important for technology leaders in both established corporations and entrepreneurial ventures.Entrepreneurial technology ventures flourish according to how well their intellectual property assets are managed, leveraged and enforced. Additionally, it is almost impossible for engineers or scientists to avoid confronting issues related to intellectual property. These include: the risk of violating the IP rights of others; an obligation to respect the IP policies of one’s employer; the need to obtain IP protection for one’s own inventions and creative works; the obligation to become involved in the management of the IP belonging to ones employer; and the challenge of ensuring that one’s own IP rights are not infringed by others, including by one’s own employer or one’s clients. In addition, with such a large amount of contemporary business—in both the private sector and government—involving outsourcing and inter-organizational collaboration, expertise in the licensing of intellectual property rights is in high demand.This course will survey basic concepts of intellectual property and provide an introduction to a variety of types of intellectual property and IP-related rights, such as patents, copyright, trade secrets, trade marks, design rights, database rights, domain names, and demarcations of origin. The will also examine the strategic management of IP in the process of technology commercialization, and the resolution of IP-related conflicts between technology based enterprises. It will also explore social, economic and ethical issues associated with the accumulation and exploitation of intellectual property.
Prerequisites:N/A

6

Kelvin Willoughby

Professor

55

MA06123 PA06123

Large-Scale Optimization and Applications

Course description:The course is devoted to optimization methods for huge-scale problems.A special attention is paid to stochastic optimization problems primarily motivated by machine learning applications. We discuss modern first order (including universal) and second order methods; smoothing techniques; mirror prox and extra gradient methods.In the second part of the course we discuss how to utilize problem structure (e.g. sparsity and separability) to speed up the optimization methods. In the final part of the course we discuss modern techniques to solve linear equations in a huge-scale setup and provide a nice relation of the very recent results there with spectral graph theory. In a recitation part of the course we focus on applied problems arises in engineering, machine learning, vision and related fields.
Prerequisites:Numerical Linear Algebra

6

Yury Maximov

Assistant Professor

56

MA06018

Machine Learning

Course description:The course covers the principal problems solved using machine learning, namely classification, regression, feature selection/extraction, density estimation, clustering and other. Major algorithms applied to solve each of these problems are studied. The course provides mathematical definition of each method, data assumptions it relies upon, together with critical analysis – its strengths and weaknesses and connections to other machine learning methods. Besides that, practical assignments for seminars and homework reinforce understanding of the course and equip students with practical understanding of possibilities/limitations of each machine learning method in real life situations.
Prerequisites:Numerical Linear Algebra

6

Evgeny Burnaev

Associate Professor

57

MA06042

Materials Chemistry

Course description:The goal of this course is to provide a survey of materials chemistry and surface spectroscopy techniques Further emphasis will be placed on interfacial chemistry of materials surfaces and ex situ and in situ study using various surface sensitive spectroscopy methodsThe course will rely on strong undergraduate math/physics background of the students, however no background in materials will be assumed or required.
Prerequisites:Undergraduate math/physics/chemistry

6

Keith Stevenson

Professor

58

MA06051

Mathematical Modeling in Engineering

Course description:Mathematical modelling is an essential tool of modern engineering. The purpose of the course is to provide the basic tools of mathematical modeling for understanding, predicting and controlling properties of realistic technologically important materials and processes as well as to develop practical skills to use them. An essential part of this course is the team work on educational projects. This work includes: formulation of the problem, identification of the most important processes underlying the problem under consideration, formulation of the problem in terms of mathematical models, analysis of the models by means of analytical tools and computer simulation, and comparison with experimental data. These projects will include examples of mathematical modeling from Electrical Engineering, Mechanics and Hydrodynamics, Energy, Space Engineering, Photonics, etc.
Prerequisites:Basic undergraduate mathematics including, linear algebra, real variables analysis, complex variables, linear algebra, differential equations, basic undergraduate physics electro-magnetism, mechanics.

6

Ildar Gabitov

Professor

59

MA02052

Molecular Biology Seminar

Course description:A research paper-based course where recent papers in the field are presented by students and critically discussed by the class led by instructor.
Prerequisites:General Molecular Biology course.

2

Konstantin Severinov

Professor

60

MA03047

Neuroscience

Course description:The course is aimed for students who are new to the field of neuroscience. We outline the basic concepts and processes of brain function ranging from molecular to cognitive neuroscience. The course aims to teach students to understand the structure and function of neuronal communication in physiology and pathology at the molecular, cellular and system levels. They will learn about brain-related diseases and pharmacology of central nervous system disorders such as ADHD, addiction, schizophrenia, bipolar disorder and Parkinson’s disease. We will also introduce common methods to study brain function. Particularly, we will introduce electrophysiological, optogenetic, imaging, voltammetric and microdialysis techniques for the study of brain function. A high emphasis will be placed on critical discussions of most up-to-date methodology
Prerequisites:Biological background, such as BS level knowledge of biochemistry, molecular and cellular biology, animal physiology.

Course description:The course provides an overview of the latest achievements in the field of material design for electronics, energy conversion and storage.The main purpose of the course is studying the basic chemical, physical and physicochemical, e.g. surface and structural, aspects of designing novel materials with the desired properties.This course will be focused mainly on organic and hybrid materials as well as on different types of electronic devices made thereof: field-effect transistors and electronic circuits, sensors, memory elements, light emitting diodes, solar cells, photodetectors, lithium and sodium batteries. Using a set of examples it will be shown how the discovery of novel materials results in the development of novel technologies, innovative products and, in some cases, even leads to revolutionary changes in specific fields of science and technology.This course is designed for MS students planning to perform experimental studies in the interdisciplinary fields at the boarder of physics and chemistry with the aim of solving relevant challenges of modern materials science.Course structure: lections, individual home assignments, final project
Prerequisites:Basic knowledge of physics and chemistry

6

Pavel Troshin

Associate Professor

62

MA06076

Petroleum Geophysics

Course description:The course will provide a graduate level overview of geophysical methods of hydrocarbon (HC) exploration; including classification, applications, integration; physical properties of rocks (density, susceptibility, resistivity, and seismic wave velocities). All types of geophysical methods will be thoroughly reviewed from a comprehensive geophysical applications but also from the standpoint of fundamental mathematical and physical principles.The course will study gravity and magnetic methods, including gravity and magnetic anomalies; ground and airborne gravity, gravity gradiometry, and magnetic surveys; principles of using gravity and magnetic data in exploration for energy resources; methods of quantitative interpretation of gravity, gravity gradiometry, and magnetic data.The course will provide a graduate level overview of geophysical inversion methods, with a major emphasis on regularization methods of solving ill-posed geophysical inverse problems. All stages of geophysical inversion techniques will be thoroughly reviewed from a comprehensive geophysical interpretation prospective but also from the standpoint of fundamental mathematical and physical principles. A special emphasis will be given to foundations of regularization theory: sensitivity and resolution of geophysical methods; smooth and focusing stabilizing functionals; definition of the regularization parameter.
Prerequisites:Basic undergraduate physics (gravity and magnetics, electromagnetics, wave propagation) and basic undergraduate mathematics (linear algebra, analysis, differential equations).

6

Marwan Charara

Associate Professor

63

MA06121

Signal and Image Processing

Course description:Nowadays, digital signals and images can be found everywhere, in thousands of scientific (e.g., astronomical, biomedical) and consumer applications (e.g., computational photography). Therefore, the ability to analyze and process digital signals and images is an extremely important skill for engineering/science master students to obtain. Indeed, digital signal and image processing is mainly responsible for the multimedia technology revolution that we are experiencing today. Important tasks that signal and image processing techniques can successfully tackle are inverse problems, such as image enhancement and restoration, which involve the removal of degradations that signals and images suffer during acquisition (e.g. removing the blur from the digital picture of a moving object, or removing the noise from a picture taken under low light conditions).This course will cover the fundamentals of signal and image processing. We will provide a mathematical framework to describe and analyze images as two- or three-dimensional signals in the spatial and frequency domains. The students will become familiar with the theory behind fundamental processing tasks including image enhancement, recovery and reconstruction. They will also learn how to perform these key processing tasks in practice using current state-of-the-art techniques and computational tools. A wide variety of such tools will be introduced including large-scale optimization algorithms and statistical methods. Emphasis will also be given on sparsity which plays a central role in modern image processing systems.The course also aims to provide students with techniques and receipts for estimation and assessment of quality of models with time series data. A special attention is paid for application of the time series analysis methods to a wide range of applied problems from engineering to finance. The course will also emphasize recent developments in time series analysis and will present some open questions and areas of ongoing research.
Prerequisites:Numerical Linear Algebra, Large Scale Optimization, High Dimensional Statistics

6

Stamatios Lefkimmiatis

Assistant Professor

64

MB06003

Space Sector Course

Course description:This course examines the domain of space from multiple vantage points — space as a business, a way of life, a fulfillment of human dreams. In addition, it examines space-related issues that drive key international regulatory, economic, and global policy. To gain insight into these different dimensions, we examine space through three different lenses: sub-sectors, technologies, and organizations.Part 1: Sub Sectors:

Launch services and markets

Satellite manufacturing and operations, including sensors and payloads

Space data products

Space communications and navigation services

Space science payloads and missions

Military space

Human spaceflight, programs and policies

For each one, we will discuss the organization of the sub-sector, what value it produces, how it is funded, the commercial and governmental organizations that participate, how it is regulated, and technology barriers

Part 2: Technologies:

Technology readiness, and sources of technology, technology planning

Launch technologies and options

Satellite technologies

Payload technologies (may need to do in parts)

Part 3: Organizations

Space sector organizations (verticals and horizontals)

“New Space”

Space agencies and other international agents (NASA, ESA, etc.)

Prerequisites:N/A

6

Edward Crawley

Invited (MIT, USA)

65

MA06075

Structure and Properties of Materials

Course description:This course is an introductory subject in the field of materials science and crystallography. The goal is to introduce students to basic concepts of structure-property relations for materials at the microscopic level.
Prerequisites:Good knowledge of general chemistry, solid state physics

6

Artem Oganov

Professor

66

N/A

DDP: Academic English

TBD

3

Elizaveta Tikhomirova

Professor

67

N/A

Academic Writing (MS)

Course description:Academic writing skills are necessary for effective research, innovation and educational activities in a multinational setting. The aim of the course is to provide guidelines and strategies for writing academic texts, focusing on relevant aspects of grammar, vocabulary and style. The course includes analysis and practice of various forms of scientific and technical writing, from memos to journal articles. It builds writing skills from sentences to paragraph structure, from summary to abstract, and to other forms of writing genres.The Course is intended for a multidisciplinary audience.Course goal: The main goal of Academic Writing in English for students is to equip students with professional English language and communication skills necessary for effective research, innovation and educational activities in a multinational setting.Course structure: integrated interactive practical seminars, individual assignments for self-study, and a written examination.

3

Elizaveta Tikhomirova

Professor

68

N/A

English for PhD Exam

Course description:Skoltech is a multinational community where English is the official language of communication used in all spheres of university life: management, education, research, and inno-vation.The course is designed for PhD students at Skoltech: young talented and ambitious researchers with entrepreneurial drive who are studying for a doctoral degree through the medium of English.The course contributes to the main mission goal of Skoltech: to educate global leaders in engineering, science, and innova-tion, advance scientific knowledge, foster new technologies to address critical issues facing Russia and the world, and generate new markets and economic opportunities based on science and technology.The aim of the course is to further develop and advance Ac-ademic English knowledge, communication skills and expe-riences obtained at the previous stages of university educa-tion. The Basic course addresses issues of Academic com-munication in general terms: it is not research field specific and is intended for a multidisciplinary audience.Course goal: the main goal of English for PhD students (Basic course) is to equip PhD students with professional English language and communication skills necessary for ef-fective research, innovation and educational activities in a multinational setting.The course also serves as a preparation for a required quali-fication English-language examination that is a prerequisite for the Thesis defense.Course structure: integrated interactive practical seminars, individual assignments for self-study, examination (a written part and an oral presentation).

3

Elizaveta Tikhomirova

Professor

Term 4 (March 27 – May 26, 2017)

69

MA06080

Advanced Bioinformatics Lab Course

Course description:
The course will introduce students to the hands-on practical analysis of novel biological “omics” data with a specific focus on the state-of-the-art analysis of the proteome, metabolome and lipidome. The course will integrate various types of omics data generated by mass spectrometry based approaches, as well as axillary data new generation sequencing technologies.
The course will include the following parts: general principles of proteomics analysis, semi-quantitative and quantitative proteomics, posttranslational modifications analysis and proteome database assembly in the proteomics section; general principles of metabolomics/lipidomics analysis, metabolite/lipid detection, quantification and annotation, metabolome/lipidome pathway analysis, systems-level analysis in the metabolomics and lipidomics sections.
The course will include practical data analysis work conducted by student in front of computer, but also introductory lectures into principles of mass spectrometry based data analysis, proteome, metabolome and lipidome organization, as well as current tools available for data analysis in these fields.
At the end of the course, students would be expected to accomplish an independent data analysis project on a model dataset including several heterogeneous types of biological “omics” data.
Prerequisites:
Bionformatics Lab Course 1.
Knowledge of basic statistics and basic molecular biology. The course is recommended for students specialized in large-scale biological data analysis (bioinformatics / computational biology / theoretical biology). The course is recommended to be taken in addition to the theoretical bioinformatics and biostatistics courses.

6

Philipp Khaitovich

Professor

70

MA03046

Advanced Molecular Biology Techniques

Course description:
This project-based course provides experience with scientific project planning and implementation in the molecular biology/biotechnology lab. Course-based research projects are geared towards the background and experience of students.The goal of the course is to teach students good laboratory practice and rational planning of the experimental work.
Prerequisites:
Basic Molecular Biology Techniques course. Basic knowledge of molecular and cellular biology as well as minimal wet lab experience.

Course description:
The course provides hand-on experience with the general techniques used in the molecular biology/biotechnology lab. The goal of the course is to teach students good laboratory practice and rational planning of the experimental work.
There are three different levels to the course (basic, advanced and research).
Prerequisites:
The course requires basic knowledge of molecular and cellular biology.

3

Konstantin Severinov

Professor

73

MA06129

Bayesian Methods of Machine Learning

Course description:The course addresses advanced methods in machine learning. The primal focus of the course are Bayesian methods for solving various machine learning problems (classification, regression, clustering, etc). Bayesian approach to probability theory allows to take into account user’s preferences in decision rule construction. Besides, it offers efficient framework for model selection. In particular, one may perform automatic feature selection, adjust the number of clusters, estimate the dimension of latent subspace, set the regularization coefficients in an efficient way. In the Bayesian framework the probability is interpreted as an ignorance measure rather than objective randomness. Simple rules for operating with probabilities such as the law of total probability and Bayes rule allow one to make inference under uncertainty conditions. In this sense Bayesian framework can be regarded as a generalization of Boolean logic.
Prerequisites:Numerical Linear Algebra

6

Evgeny Burnaev

Associate Professor

74

MA06044

Carbon Nanomaterials

Course description:The course covers the subject of carbon nanomaterials (fullerenes, nanodimond, nanotubes, and graphene). The history of carbon compounds since antiquity till our days starting from charcoal to carbon nanotubes and graphene will be reviewed. The students will have opportunity to synthesize carbon nanotubes (by aerosol and CVD methods) and graphene, to observe the materials in transmission (TEM) and scanning (SEM) electron microscopes as well as by an scanning tunnelling (STM) and atomic force (AFM) microscopes and to study optical and electrical properties of the produced carbon nanomaterials.
Prerequisites:BS students in Physics, Chemistry or related fields

6

Albert Nassibulin

Professor

75

MA06134

Cell Biology Lab Course

Course description:Lab course in Cell biology provides students an opportunity to explore how the techniques of molecular and cell biology may be used to understand cell function. Laboratory practice in cell biology will provide the experience in genetic manipulations with cell lines, immunostaining and fluorescence or confocal microscopy analysis. The main aspects for FACS-analysis and cell sorting will be introduced. The approaches for gene expression analysis by RT-qPCR, Western blot and differential proteome analysis will be used to understand the influence of genetic manipulation to the cell function. The introduction in powerful approach to understand the protein interaction such as SPR optical biosensor will be provided.The introduction in high-throughput screening of biologically active compounds will be provided.The course will provide students with a hands-on understanding of modern methods of cellular manipulation and understanding the mechanism of cell functioning.
Prerequisites:Basic Molecular Biology Techniques course

6

Olga Dontsova

Professor

76

MA06133

Comparative Genomics

Course description:TBD
Prerequisites:TBD

3

Mikhail Gelfand

Professor

77

MA06017

Composite Materials and Structures

Course description:This course aims to provide knowledge about manufacturing, properties, and contemporary problems in composite materials. The emphasis is on the practical applications, theoretical background, and the use of composite materials in industry. The course cuts across several domains, covering mechanics of materials, design, manufacturing, and in service issues:Introduction: What is a composite? Classification. Metals vs composites, advantages and disadvantages. Applications in industry.Fibers. Matrices. Micromechanics.Mechanics: Stresses and strains. Ply. Laminate theories. Hygrothermal strains and stresses. Edge effect. Interlaminar stresses.Manufacturing: Unidirectional vs. textile. Thermoplastic vs. thermoset. Prepreg vs. infusion.Failure criteria.Defects. Fractal defect structures.Fatigue. Delaminations. Damage tolerance.Finite element analysis. Abaqus.Participants will learn fundamentals of these areas through active participation in teamwork. The course will provide practical knowledge on applications of composite materials in aerospace and mechanical engineering.The course includes practical experience of composite manufacturing and mechanical tests. During the last part of the course the participants will be presented a ‘challenge’ project in design and structural analysis, which they may attack experimentally, analytically or by means of finite-element package Abaqus. Participants are expected to demonstrate their collective knowledge while at the same time solving individually a real problem.
Prerequisites:Course Structural Analysis and Design (or the equivalent course).

6

Stepan Lomov; Sergey Abaimov

Invited (KU Leuven, Belgium) +Skoltech Senior Research Scientist

78

MA06079

CSE II: Discretization

Course description:Many scientific models are formulated in terms of differential or integral equations and describe continuous quantities, such as the distribution of velocity of a fluid in a space outside an aircraft wing, distribution of stress in a solid body, price of a stock as a function of time, etc. In order to use these models in a computer simulation, the models must be discretized. The course covers a representative selection of methods of discretization of differential and integral equations. The emphasis of the course is on practical aspects of using discretization methods: intuitive understanding and formal derivation of accuracy of different methods, modelling, testing and optimizing real mechanical systems, and solving applications-informed practical problems.
Prerequisites:CSE I

6

Alexander Shapeev

Assistant Professor

79

MA06057

Deep Learning

Course description:Representation learning (and deep learning as its most important particular case) is arguably the hottest topic in Machine Learning. Over the last few years, deep learning has led to several breakthroughs across a variety of application domains, including speech recognition, computer vision, and, more recently, natural language processing, and bioinformatics. The course will cover the basics and the recent advances on RDL. The course will be practical in nature, with intense work on Python/MATLAB assignments, and application projects on large-scale machine learning.While there will be a certain bias towards computer vision/image data, other application domains will be also covered in details.Preliminary syllabus:

Course description:
The course covers the subject of carbon nanomaterials (fullerenes, nanodimond, nanotubes, and graphene). The history of carbon compounds since antiquity till our days starting from charcoal to carbon nanotubes and graphene will be reviewed. The students will have opportunity to synthesize carbon nanotubes (by aerosol and CVD methods) and graphene, to observe the materials in transmission (TEM) and scanning (SEM) electron microscopes as well as by an scanning tunnelling (STM) and atomic force (AFM) microscopes and to study optical and electrical properties of the produced carbon nanomaterials.
Prerequisites:BS students in Physics, Chemistry or related fields

6

Keith Stevenson

Professor

81

MA03073

Genetic Engineering

Course description:This course will survey basic concepts of genetic manipulation and provide an introduction to a variety of novel approaches applicable for basic research on gene structure and function?, production of useful proteins, generation of transgenic plants and animals, medical diagnosis and treatment?, and genome analysis. The course will also examine the major genetic manipulation strategies accepted in modern biotechnology and medicine. It will also explore environmental, social, economic and ethical issues associated with the genetic engineering and molecular cloning.
Prerequisites:N/A

3

Konstantin Piatkov

Assistant Professor

82

TBC

Geometrical Methods of Machine Learning

Course description: TBD

6

Alexander Bernstein

Principal Research Scientist

83

MA06085

Geostatistics and Reservoir Simulation

Course description:The course includes lectures in geostatistics and reservoir simulation including fundamentals of single point and multi-point statistics, variance, and Gaussian simulation. The reservoir simulation lectures introduce the mathematics and practical portions of reservoir simulation. Laboratory computational exercises are also included.
Prerequisites: N/A

6

Dmitri Koroteev

Assistant Professor

84

MA06128

High-Dimensional Statistical Methods

Course description:Modern data analysis faces the new challenges due to high dimension and complexity of the considered models like microarrays, image- and video data, network data, etc. The standard methods of statistical theory fail in such situations. Even for classical linear models, linear methods known to be optimal in the usual framework appear to be extremely inefficient for large parameter dimension. New ideas and approaches have been developed in the last years, mainly based on the notion of sparsity and sparse recovery. This course gives an introduction in this modern area of research.
Prerequisites:Machine Learning

6

Vladimir Spokoiny

Professor

85

MA06097

Human Spaceflight

Course description:It covers the physiological implications of human spaceflight, both suborbital and orbital, the current approaches for long term care for astronauts and the future challenges during exploration Missions. It also focusses on the aspect of future spacecraft and space habitat design from the standpoint of human health and performance. It also covers aspects of cellular and molecular adaptation to altered gravity as well as the field of Astrobiology. It also covers the application potential of technology developed for human spaceflight on earth.
Prerequisites:No specific background required; biomedicine knowledge helpful.

6

Rupert Gerzer

Professor

86

MA06007

Introduction to Power Systems

Course description:The course will provide a graduate level overview of modern power systems, with a major emphasis on the system aspects of power production, transmission, storage and delivery. All stages of the power energy technical chain will be thoroughly reviewed from a comprehensive engineering prospective but also from the standpoint of fundamental physics principles/equations. A special emphasis will be given to improving students’ ability to extract well-formulated physics and mathematics problems from power engineering reality.The course will rely on strong undergraduate math/physics background of the students, however no background in power systems will be assumed or required. In this course we will advance gradually through major principles of the power system design (with some history re-course), discussion of major elements of the power systems (generators, power lines, transformers), analyze state estimation, optimization, control and design practice, and will conclude with discussions of modern engineering, physics and mathematics challenges associated with the emergent smart/intelligent/resilient grid technologies.
Prerequisites:Basic undergraduate physics (mechanics, electro-magnetism) and basic undergraduate mathematics (linear algebra, analysis, differential equations).

6

Petr Vorobev

Invited (MIT, USA)

87

MA06148

Introduction to Product Lifecycle Management (PLM)

Course description:This newly developed course introduces Product Lifecycle Management (PLM) concepts and tools with concentration on practical examples of PLM Systems’ implementation in high-tech industrial sectors. Students will be acquainted with major PLM systems on the market, their capabilities, market strategies of the providers, service arrangements, and with major customers. Historical perspectives and opportunities for implementation of these systems and tool will be addressed, discussed, and analyzed in the theoretical part of the course and in the students’ assignments. Invited lecturers will share their experience with introduction and use of PLM Systems in all phases of their companies’ business cycles. Students will discuss results and prospects of these systems’ use with representatives of these companies. A special sector of this course will be devoted to Configuration Management II – the basis for implementation of PLM in any business and/or design/development/manufacturing activities. Students, attending all sessions of this segment of the course will get official internationally recognized certificates of completion from the Configuration Management Institute (the U.S. leading organization in the development of standards and in certification of systems engineers). During the whole course, the students will work within Siemens Teamcenter environment while going through an extensive self-paced training program in the Siemens PLM tools (LMS, NX, Technomatix). The students will learn basics of parametric and configuration optimization tools on an example of application of the P-7 system. The students will apply obtained knowledge and skills in a two (earlier in the program defined) team assignments resulted in team presentations. An examination commission, consisting of CDMM faculty and of invited members, will conduct final evaluation of the overall product design completeness, quality of the results achieved, and of the team presentations delivered.
Prerequisites:N/A

6

Ighor Uzhinsky

Professor

88

MA02052

Molecular Biology Seminar

Course description:A research paper-based course where recent papers in the field are presented by students and critically discussed by the class led by instructor.
Prerequisites:General Molecular Biology course.

2

Konstantin Severinov

Professor

89

MA06131

Natural Language Modeling and Processing

Course description:The main purpose of this course is to introduce the basic concepts needed for computational processing of human languages.The course is aimed at understanding the specifics of Natural Language as an object of computational analysis, the ability to choose the proper linguistic model (linguistic features related to different levels of NL system) and the proper method to address the problem and carry out meaningful linguistic interpretation of results.By the end of this course the students should have clear understanding of the issues involved in the main tasks of document classification and text analysis. They will have to complete a mini-project aimed at providing a completed system for one of the tasks. These projects can be carried out both on the basis of open source technologies and in the frameworks of special scientific programs such as ABBYY Compreno Based Research, large-scale Internet corpora (GIKR) projects or DialogueEvaluation NLP-testing tracks.
Prerequisites:Theoretical computer science (automata, context-free grammar (CFG),etc)Elements of Optimization Theory (Markov Chains, Machine Learning Basics)Probability theory and statisticsMathematical Logic basics

6

Vladimir Selegey and Sergey Sharov

Invited (ABBYY, Russia)

90

MA06094

Quantum Fluids

Course description:Superfluidity is the central topic across many fields of physics, including condensed matter, quantum field theory, critical phenomena, classical hydrodynamics and nuclear matter. In the last two decades the field has undergone an important transformation combining theory with experimental realisations and potential applications. The course presents an overview of superfluidity with emphasis on properties of various quantum fluids from superfluid helium to atomic condensates and solid_state condensates.
Prerequisites:Recommended knowledge: Basic knowledge of quantum mechanics, statistical physics and fluid dynamics;Recommended courses: Course «Introduction to Condense Matter», or a comparable course.

6

Natasha Berloff

Professor

91

PA06144

Quantum Materials

Course description:Superfluidity is the central topic across many fields of physics, including condensed matter, quantum field theory, critical phenomena, classical hydrodynamics and nuclear matter. In the last two decades the field has undergone an important transformation combining theory with experimental realisations and potential applications. The course presents an overview of superfluidity with emphasis on properties of various quantum fluids from superfluid helium to atomic condensates and solid_state condensates.
Prerequisites:Recommended knowledge: Basic knowledge of quantum mechanics, statistical physics and fluid dynamics;Recommended courses: Course «Introduction to Condense Matter», or a comparable course.

6

Mikhail Skvortsov

Associate Professor

92

MA06135

RNA Biology

Course description:This course will focused at the knowledge about RNA and RNA-protein complexes structures as well as the RNA-protein complexes functioning. The aim of this course is to provide a gentle exploration of the modern basics techniques of RNA and RNA-protein structure and RNA modifications analysis. By focusing on fundamental mechanisms such as translation, splicing and gene expression regulation the role of RNA in cell identity maintenance and cell metabolism.
Prerequisites:Knowledge of basic molecular biology

3

Timofey Zatsepin

Associate Professor

93

MA06056

Smart Grids

Course description:The course will build on the Introduction to Power Systems course, developing at graduate level a wide range of topics in recent smart grid development as detailed below. The goal is to give students familiarity with such topics and competence in handling them, in the context of present and future distribution and transmission networks.
Prerequisites:Basic undergraduate mathematics and physics are assumed. It is also expected that students will have taken the Introduction to Power Systems course and be familiar with its content.

6

Phil Taylor, Padraig Lyons, Neal Wade

Invited (Newcastle University, UK)

94

MA06098

Space Data Processing

Course description:The course introduces students to practically useful approaches of data processing for control and forecasting. The focus is on identifying the hidden and implicit features and regularities of dynamical processes using experimental data. The course exposes data processing methods from multiple vantage points: standard data processing methods and their hidden capacity to solve difficult problems; statistical methods based on state-space models; methods of extracting the regularities of a process on the basis of identifying key parameters. The course addresses the problems in navigation, solar physics, geomagnetism, space weather and biomedical research.
Prerequisites:Basic knowledge of probability theory and mathematical statistics.

6

Tatiana Podladchikova

Assistant Professor

95

MA06084

Stochastic Modeling and Computations

Course description:
The course shall be considered as a “soft” and self-contained introduction to modern “applied probability” covering theory and application of stochastic models. Emphasis is placed on intuitive explanations of the theoretical concepts, such as random walks, law of large numbers, Markov processes, reversibility, sampling, etc., supplemented by practical/computational implementations of basic algorithms. In the second part of the course, the focus will shift from general concepts and algorithms per se to their applications in science and engineering with examples, aiming to illustrate the models and make the methods of solution clear, from physics, chemistry, machine learning, control and operations research discussed.
Prerequisites:
Solid preparation in practical math (ability to solve problems in linear algebra, calculus, and differential equations) will be required from anybody taking this course. Basic “user” knowledge of a high-level scientific programming language (matlab, python, julia or mathematica) will be needed for completing homework assignments.

6

Michael Chertkov

Professor

96

MA06010

Systems Architecture

Course description:
This course introduces students to formal methods of systems architecting and tradespace exploration for early conceptual design of engineering systems.
The course reviews the theory of systems architecting and introduces students to quantitative tradespace exploration, with exposure to computational methods for architectural enumeration, evaluation, and downselection.
Methods reviewed in this course include tools for creativity and conceptual design, formal systems design synthesis (UML, OPM), design of experiments techniques, correlation analysis, sensitivity analysis, systems modeling and simulation.
Students will apply the theory discussed in the course to an individual design project of their choice. After successful completion of the course, students will acquire a toolkit of qualitative and quantitative methods to support the architecting process of new engineering systems in their domain of expertise (i.e. Space, Energy, IT, Nuclear, Biomed).
Prerequisites:
N/A

6

Alessandro Golkar

Associate Professor

97

PA06140

Theoretical Foundations of Computer Science

Course description:
In this course we introduce the cardinal topics of modern research in data science, and familiarize PhD students with fundamental solutions to research problems in this area. In particular, we introduce fundamental principles of data system architecture; we discuss massive data analysis, and we examine the management of very large data systems, including questions of adaptivity and self-tuning; we present the fundamentals of data models and languages, especially in relation to semi-structured data, multi-media, temporal and spatial data; we analyze the problems of privacy, security, and trust in data systems; we analyze techniques for recognition, image analysis, computer vision, statistical methods for learning, representations for recognition and localization. We investigate methods and algorithms for analyzing scientific data, social network analysis, recommender systems, mining sequences, time series analysis, online advertising, text/web analysis, topic modeling, mining temporal and spatial data, graph and link mining, rule and pattern mining. We introduce the concepts of dimensionality reduction and manifold learning, combinatorial optimization, relational and structured learning, classification and regression methods, semi-supervised learning, unsupervised learning including anomaly detection and clustering, kernel methods, compressed sensing and sparse modeling, graphical models, Bayesian methods, deep learning, hyper-parameter and model selection, Markov decision processes, reinforcement learning, dynamical systems and Hidden Markov Processes, recurrent networks.The course aims to bring all students on the same page regarding the nature and orientation of state-of-the-art work in their field, so that they acquire both depth and breadth of knowledge.
Prerequisites:
Linear Algebra, Calculus, Differential equations, Mathematical modeling, Numerical methods, Probability, Programming, Numerical Linear Algebra, Large Scale Optimization, High Dimensional Statistics.

6

Evgeny Burnaev

Associate Professor

98

MA06053

Thermal Fluid Sciences

Course description:
Thermal-Fluid Sciences course is designed for Skoltech students who need exposure to key concepts in the thermal-fluid sciences in order to successfully apply them in research programs of various Skoltech CREIs. The text is made up of three parts: (1) Thermodynamics, (2) Fluid Mechanics, and (3) Heat and Mass Transfer.
Prerequisites:
Calculus, Differential Equations (ODE and PDE), General Physics.